About

Armin Sorooshian, Ph.D., is the Principal Investigator of the Sorooshian Group at the University of Arizona. Raised in Tucson, Arizona, he completed his undergraduate studies at the University of Arizona, earning a B.S. in Chemical Engineering in 2003. He then pursued his doctoral studies at the California Institute of Technology (Caltech), where he obtained a Ph.D. in Chemical Engineering in 2008. Following his Ph.D., Dr. Sorooshian conducted a one-year postdoctoral fellowship through the Cooperative Institute for Research in the Atmosphere (CIRA), a collaboration between Colorado State University and the National Oceanic and Atmospheric Administration (NOAA). In 2009, he returned to the University of Arizona as an assistant professor, where he continues his academic and research career.

Research topics

• Geology
• Climatology
• Meteorology
• Environmental science
• Geography
• Atmospheric sciences
• Physics
• Political Science
• Oceanography
• Nursing
• Virology
• Medicine
• Engineering
• Physical geography
• Engineering ethics
• Public relations

Selected publications

• Characteristics of Northwest Atlantic Cloud Water: PMF Applied to ACTIVATE’s Cloud Water Data Set

  ACS Earth and Space Chemistry · 2026-04-08

  articleSenior authorCorresponding

  Cloud water serves as a chemical reactor where gases and particles undergo chemical transformations and thus is a key component in aerosol-cloud interactions. A total of 535 cloud water samples collected aboard the NASA ACTIVATE HU-25 Falcon (2020–2022) over the northwest Atlantic (U.S. East Coast to Bermuda) are analyzed, and a set of 31 dissolved species spanning major inorganic ions, selected organic acids, and trace elements are used here for source apportionment analysis. The EPA positive matrix factorization (PMF) 5.0 model yields a six-factor solution that explains considerable variability in dissolved solute mass (r = 0.77), with the factors being sea salt (81.3% of reconstructed mass), secondary aerosol (12.2%), traffic/combustion (5.2%), metal-enriched dust (0.9%), aged dust (0.3%), and an industrial/metallurgical factor (0.07%). Seasonal and spatial patterns showed strong sea salt mass contributions in all months, but especially in winter enhanced aged dust influence near Bermuda in June, and larger relative contributions of metal and traffic/combustion-related factors near the coast. Concentration-weighted trajectory maps link the dust factor to trans-Atlantic transport from North Africa and most other factors to export from the eastern United States. Overall, the results show that mass-based cloud water chemistry is strongly weighted toward larger, highly hygroscopic particles, while still retaining clear signatures of continental pollution and long-range dust transport.

  PublisherDOI

• Secondary Ice Production Occurs Predominantly between -8 C to -3 C in Midlatitude Winter Mixed-Phase Marine Cold Air Outbreaks

  2026-04-21

  article
  PublisherDOI

• Below‐Cloud CCN Concentrations Provide Better Constraints on the Twomey Effect Than Aerosol Optical Properties

  Geophysical Research Letters · 2026-04-25

  articleOpen access

  Abstract The primary uncertainty in anthropogenic climate forcing arises from a limited understanding of aerosol effects on cloud albedo, which in combination with other effects, is termed the radiative forcing from aerosol‐cloud interactions (RF aci ). Although climate models provide estimates of RF aci , observational constraints remain critical for reducing its uncertainty. Observationally based estimates of RF aci traditionally have been inferred from large‐scale satellite relationships between aerosol and cloud properties, but these approaches rely on substantial assumptions. Here, we develop a novel framework that investigates cloud responses to aerosol variability using Machine Learning (ML) derived cloud condensation nuclei (CCN) profiles from lidar, combined with polarimetric cloud retrievals. Our results demonstrate that the ML‐CCN product consistently improves estimates of CCN‐cloud relationships. By providing vertically resolved CCN information and avoiding complications from aerosol humidification and vertical heterogeneity, this approach yields tighter and more physically plausible constraints on aerosol‐cloud interactions than conventional methods based on aerosol optical properties.

  PublisherDOI

• Optimization of spatio-temporal ozone (O3) pollution modeling using an ensemble machine model learning with a swarm-based metaheuristic algorithm

  Ecotoxicology and Environmental Safety · 2025-07-30

  articleOpen access

  pollution on human health and the environment.

  PublisherDOI

• Intercomparison of wind speed, temperature, and humidity data between dropsondes and aircraft in situ measurements

  Atmospheric measurement techniques · 2025-09-09 · 1 citations

  articleOpen accessSenior author

  Abstract. Airborne measurements of wind speed and direction, temperature, and relative humidity are critical due to their importance for atmospheric processes. Field campaigns with multiple coordinated aircraft present challenges when combining data from each platform due to atmospheric heterogeneity. To confront this issue, this work intercompares for the first time in situ measurements from the Turbulent Air Motion Measurement System (TAMMS) of horizontal winds and temperature and a diode laser hygrometer (relative humidity) deployed on an HU-25 Falcon flying mostly within the marine boundary layer to an independent set of measurements from dropsondes launched from a higher-flying King Air. Leveraging data from 162 joint flights over the northwest Atlantic from these two spatially coordinated aircraft during the NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) campaign in winter and summer seasons between 2020–2022, a total of 555 pairs of Falcon–dropsonde data points are identified within 30 km horizontal separation, with minimal vertical separation (usually < 1 m), and within 15 min. This analysis is based on the following range of conditions experienced: altitude = ∼ 0.1–5 km, temperature = −19–27 °C, relative humidity = 1 %–100 %, and wind speed = 0.2–42 m s−1. Based on scatterplots, correlation coefficients, and mean (in situ–dropsonde) error (ME), intercomparisons reveal good agreement for wind speed (r = 0.95, ME = 0.21 ± 1.68 m s−1), u/v wind components (r ∼ 0.96–0.97, ME ∼ 0.03–0.16 (± 1.62–1.67) m s−1), wind direction (r = 0.94, ME = 0.00 ± 0.22 based on cosine of direction angles), temperature (r = 0.99, ME = 0.00 ± 0.71 °C), and relative humidity (r = 0.91, ME = −3.86 ± 10.74 %). Sensitivity analysis shows that binning data into categories of horizontal separation distance, clear versus cloud, winter versus summer, altitude range, and terciles of the values for examined variables did not yield major changes except for relative humidity where there was more deviation, especially above 70 %. The effect of statistics was examined by relaxing the vertical separation distance criteria to expand the number of pairs to over 360 000, without much difference in intercomparison metrics. The effect of averaging more points for each instrument in the final 555 pairs was also shown to lead to minimal change in agreement. Overall, these results provide confidence in both the performance of the measurement techniques compared and combining dropsonde data with in situ data from a separate coordinated aircraft for ACTIVATE, which has relevance to other campaigns with multiple coordinated aircraft conducting similar types of measurements.

  PublisherDOI

• Can a Weekly Cycle in Pollutants be Observed Offshore the U.S. East Coast with Airborne Measurements?

  ACS ES&T Air · 2025-10-22 · 1 citations

  articleSenior authorCorresponding

  Varying anthropogenic emissions during a typical week can lead to shifts in aerosol properties, yet such signals are rarely examined in marine outflow regions. Since aerosol data from the IMPROVE network along the U.S. East Coast show weekday peaks in anthropogenically influenced aerosol species, this study leverages airborne measurements from NASA’s ACTIVATE campaign (2020–2022) to investigate weekly cycles of aerosol properties over the Northwest Atlantic Ocean. Analysis focuses on winter and summer flights that sampled continental outflow along repeated transects between NASA Langley Research Center (LaRC) and two aviation waypoints (ZIBUT and OXANA). Aerosol number concentration (N) between 0.1 and 1 μm (N0.1–1 μm), NCCN at 0.37–0.43% supersaturation, and aerosol surface area concentration (SA0.003–1 μm) show midweek peaks for LaRC-OXANA and LaRC-ZIBUT, with minima on Sunday/Monday. However, the most pronounced weekly cycle for both corridors was for variables relevant to new particle formation (NPF: N3 nm, N3 nm–N10 nm, N0.01–0.1 μm, N3:N10, where N3:N10 is the ratio of particles greater than 3 nm to those greater than 10 nm), which peak earlier in the week (Sunday/Monday) and decrease into the work week. While IMPROVE data show a weekly cycle for many variables along the coast (PMcoarse, PM2.5, fine soil, sulfate, and nitrate), airborne data show that organics exhibit consistent peaks on Thursday and minima on Sunday for both corridors. The weekly cycles are generally robust (especially NPF-related variables) when isolating wintertime data and when dividing LaRC-OXANA and LaRC-ZIBUT corridors in half. The results provide evidence for a weekly cycle for some aerosol characteristics offshore of a major continent with implications for cloud microphysics, remote sensing, and downwind air quality.

  PublisherDOI

• Lead sources detected in Manila's air after the phase-out of leaded gasoline

  Atmospheric Environment · 2025-12-02

  article
  PublisherDOI

• Aerosol effective radius governs the relationship between cloud condensation nuclei (CCN) concentration and aerosol backscatter

  Atmospheric chemistry and physics · 2025-10-27 · 1 citations

  articleOpen access

  Abstract. Understanding the vertical distribution of cloud condensation nuclei (CCN) concentrations is crucial for reducing uncertainty associated with aerosol–cloud interactions (ACIs) and their effective radiative forcing. Many studies take advantage of widely available remote sensing observations to develop proxies, parameterizations, and relationships between CCN concentration and aerosol optical properties (AOPs). Such methods generally provide a good constraint for CCN concentration, but many uncertainties and limitations exist, generally related to high relative humidity (RH), environments with internal or external mixtures of several different aerosol types, and differences in parts of the aerosol size distribution relevant to both CCN and AOPs. In this study, we use in situ observations of the aerosol size distribution and chemical composition in a recent airborne field campaign to inform theoretical calculations of CCN concentration (CCNtheory) and aerosol backscatter at 532 nm (BSCtheory) with the purpose of understanding the dominant governing factors of the CCNtheory–BSCtheory relationship. Estimates from random forest models indicate that, for smoke, marine, and urban aerosols, the aerosol size distribution, as parameterized by the effective radius (Reff), is the most important predictor of the CCNtheory–BSCtheory relationship. We further investigate how Reff impacts CCNtheory : BSCtheory and find an exponential relationship between the parameters. We find that modeling CCNtheory : BSCtheory using this exponential Reff relationship can explain about 68 %–79 % of the variance in the CCNtheory–BSCtheory relationship. These findings suggest that including information about aerosol size is critical for future studies in constraining CCN concentration from AOPs.

  PublisherOA PDFDOI

• Aerosol Fine Mode Fraction Retrievals for the Marine Boundary Layer From Airborne Lidar

  Journal of Geophysical Research Atmospheres · 2025-10-27

  articleOpen accessSenior author

  Abstract Separating contributions of the fine and coarse modes is important for characterizing aerosols and assessing their impacts. This work develops retrievals of fine mode fraction (FMF) from lidar observables for the marine boundary layer (MBL) using data collected during the ACTIVATE field campaign. First, we calculate multiwavelength backscatter and extinction and derived metrics for spherical particles derived from measured size distributions (combining in situ aerosol and cloud probes) and hygroscopicity estimates. The calculations show reasonable skill when compared to airborne High Spectral Resolution Lidar—generation 2 (HSRL‐2) retrievals, displaying low biases and explaining up to 87% of the variability in backscattering. While slopes are generally close to 1:1 for lidar ratios and Angstrom exponents (AEs), the variability within HSRL‐2 data is only well captured for lidar ratios (50%–67%). Having established that the calculated optical properties are representative of remotely sensed ones in the marine environment, they are used together with in situ aircraft particle size data to train multilinear regression models to estimate FMF proxies (extinction FMF, PM 1 /PM 10 and PM 2.5 /PM 10 ratios). When tested with HSRL‐2 observations as inputs, these models can represent up to 67%–78% of the variability of the observed FMF proxies with biases at high FMFs that depend on the accuracy of the coarse mode aerosol size measurements. The regression retrievals are tested for lidar transects and show expected gradients due to continental influence on the MBL and differential hygroscopicity of fine versus coarse mode aerosol with height. These results are encouraging for their application for various lidar systems.

  PublisherDOI

• Closing the gap: an algorithmic approach to reconciling in-situ and remotely sensed aerosol properties

  Atmospheric measurement techniques · 2025-12-02

  articleOpen access

  Abstract. Remote sensing retrievals of atmospheric particle (i.e., aerosol) properties, such as those from lidars and polarimeters, are increasingly used to study aerosol effects on critical cloud and marine boundary layer processes. To ensure the reliability of these retrievals, it is important to validate them using aerosol measurements from in-situ instruments (i.e., external closure). However, achieving rigorous external closure is challenging because in-situ instruments often (1) provide dry (relative humidity (RH) < 40 %) aerosol measurements, while remote sensors typically retrieve properties in ambient conditions and (2) only sample a limited aerosol size-range due to sampling inlet cutoffs. To address these challenges, we introduce the In-Situ Aerosol Retrieval Algorithm (ISARA), a methodological framework designed to enable closure between in-situ and remote sensing aerosol data by converting dry in-situ aerosol optical and microphysical properties into their humidified equivalents in ambient air. We apply ISARA to aerosol measurements collected during the NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) field campaign to test its ability to generate aerosol properties that are physically consistent across in-situ and remote sensing platforms. To assess this performance, we conduct consistency analyses comparing ISARA-calculated intensive and extensive aerosol properties with corresponding measurements from (1) ACTIVATE's in-situ instruments (internal consistency), (2) Monte Carlo in-situ data simulations (synthetic consistency), (3) ACTIVATE's Second Generation High Spectral Resolution Lidar (HSRL-2) and Research Scanning Polarimeter (RSP) instruments (external consistency). This study demonstrates that: (1) appropriate a priori assumptions for aerosol can lead to consistency between many in-situ measurements and remote sensing retrievals in the ACTIVATE campaign, (2) ambient aerosol properties retrieved from dry in-situ and the RSP polarimetric data are compared showing reasonable agreement for the first time in literature, (3) measurements are externally consistent even in the presence of moderately absorbing (imaginary refractive index (IRI) > 0.015) and coarse nonspherical particles, and (4) ISARA is likely limited by (i) under-sampling of low background concentrations (N < 1 cm−3) for aerosol sizes greater than 5 µm in diameter as well as (ii) by an under-determined measurement system. These results suggest that additional in-situ measurements under ambient conditions, at a wider range of wavelengths, of the real refractive index, and of the coarse aerosol size distribution, can reduce the uncertainties of the in-situ ambient aerosol products. Although this study focuses on fine spherical aerosol mixtures with a coarse mode that is spherical or nonspherical (spheroidal), its success demonstrates that ISARA could have the potential to support systematic and physically consistent closure of aerosol data sets in various field campaigns and aerosol regimes.

  PublisherDOI

Recent grants

• Collaborative Research: Using Controlled Aerosol Perturbations to Improve Understanding of Cloud Responses for Climate

  NSF · $120k · 2010–2014

Frequent coauthors

• Ewan Crosbie

  Langley Research Center

  256 shared

• John H. Seinfeld

  California Institute of Technology

  146 shared

• Luke D. Ziemba

  140 shared

• Taylor Shingler

  Langley Research Center

  130 shared

• Richard C. Flagan

  California Institute of Technology

  128 shared

• Claire Robinson

  University Hospitals of Leicester NHS Trust

  122 shared

• Edward L. Winstead

  111 shared

• Michael A. Shook

  Langley Research Center

  106 shared

Labs

• Sorooshian GroupPI

Education

• PhD, Chemical Engineering

  California Institute of Technology

  2008

• BS, Chemical and Environmental Engineering

  University of Arizona

  2003

Awards & honors

• University Distinguished Scholar
• da Vinci Fellow